Systems and methods for determining remaining useful energy in an electric aircraft

ABSTRACT

A system for determining remaining useful energy in an electric aircraft, the system including a computing device where the computing device is configured to measure a internal state datum of a battery as a function of at least a sensor, receive the internal state datum from the at least a sensor, generate a useful energy remaining datum as a function of the internal state datum and a battery model, and display the useful energy remaining datum to a user.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircraft. In particular, the present invention is directed to systemsand methods for determining remaining useful energy in an electricaircraft.

BACKGROUND

The range of how far an electric vehicle can go is based on how muchenergy remains in the battery pack, but often not all the energy canused for operating the vehicle due to a plurality of factors, so it isuseful for a user to be able to know with accuracy how much usefulenergy remains in the battery.

SUMMARY OF THE DISCLOSURE

In an aspect a system for determining remaining useful energy in anelectric aircraft, the system including a computing device where thecomputing device is configured to measure an internal state datum of abattery as a function of at least a sensor, receive the internal statedatum from the at least a sensor, generate a useful energy remainingdatum as a function of the internal state datum and a battery model, anddisplay the useful energy remaining datum to a user.

In another aspect a method for determining remaining useful energy in anelectric aircraft, the method including measuring, by a computingdevice, an internal state datum of a battery as a function of at least asensor, receiving, by the computing device, the internal state datum forthe at least a sensor, generating, by the computing device, a usefulenergy remaining datum as a function of the internal state datum and abatter model, and displaying, by the computing device, the useful energyremaining datum to a user.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating a system for determiningremaining useful energy in an electric aircraft;

FIG. 2 is an exemplary flow diagram of a method for determiningremaining useful energy in an electric aircraft.

FIG. 3 is an illustrative embodiment of a battery;

FIG. 4 is an exemplary embodiment of an electric aircraft;

FIG. 5 is an illustrative diagram of a flight controller;

FIG. 6 is an exemplary embodiment of a machine learning module;

FIG. 7 is an illustrative embodiment of a neural network;

FIG. 8 is an exemplary embodiment of a neural network node; and

FIG. 9 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for determining remaining useful energy in anelectric aircraft. In an embodiment, the system includes a computingdevice configured to measure an internal state datum of a battery basedon data from at least a sensor, the computing device receives the datafrom the at least a sensor, generates a useful energy remaining datumbased on the internal state datum and a battery model, the computingdevice then displays the useful energy remaining datum to a user.

Aspects of the present disclosure can be used to provide a fleet manageror any other user with information related to how much useful energyremains in the battery. Aspects of the present disclosure can also beused to calculate the remaining flight range of the aircraft. This isso, at least in part, because the system generates the useful energyremaining in the battery.

Aspects of the present disclosure also allow for generating a predictedthermal runaway for the battery. This is so, at least in part, becausethe system, by a computing device, measures the temperature of thebattery and generates a thermal datum based on data from the sensors andthe battery thermal model. Exemplary embodiments illustrating aspects ofthe present disclosure are described below in the context of severalspecific examples.

Referring now to FIG. 1, an exemplary embodiment of a system 100 fordetermining remaining useful energy in an electric aircraft isillustrated. System 100 includes a computing device 104. Computingdevice 104 may include any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Computing device 104 may include a singlecomputing device operating independently, or may include two or morecomputing device operating in concert, in parallel, sequentially or thelike; two or more computing devices may be included together in a singlecomputing device or in two or more computing devices. Computing device104 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting computing device104 to one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Computing device 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device 104 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

With continued reference to FIG. 1, computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing. In someembodiments, the computing device 104 may be a flight controller. Flightcontroller is described in detail further below.

Still referring to FIG. 1, computing device 104 is configured to measureinternal state datum of a battery 108 as a function of at least a sensor112. In an embodiment, the internal state datum may be calculated by thecomputing device 104. In some embodiments, the internal state datum maybe calculated by the at least a sensor 112. An “internal state datum,”for the purpose of this disclosure, includes any parameters usable todetermine an internal state of a battery. In an embodiment, internalstate datum may include an internal resistance and/or impedance of thebattery. In nonlimiting embodiments, generating the remaining usefulenergy datum includes any methods and or parameters used to determine astate of charge of a battery. In one embodiment, internal state datummay further include other data related to the battery such astemperature. Battery 108, also referred herein as battery pack, isdescribed in detail further below. In a nonlimiting example, computingdevice 104 may utilize a resistance sensor designed and configured tomeasure the resistance of the battery 108.

Continuing to refer to FIG. 1, computing device 104 is configured toreceive the internal state datum from the at least a sensor 112. In someembodiments, at least a sensor 112 may comprise a plurality of sensorsin the form of individual sensors or a sensor suite working in tandem orindividually. At least a sensor 112 may include a plurality ofindependent sensors, as described herein, where any number of thedescribed sensors may be used to detect any number of physical orelectrical quantities associated with an aircraft power system or anelectrical energy storage system. At least a sensor 112 may include aresistance sensor designed and configured to measure the resistance ofat least an energy source. At least a sensor 112 may include separatesensors measuring physical or electrical quantities that may be poweredby and/or in communication with circuits independently, where each maysignal sensor output to a control circuit such as a user graphicalinterface. In a non-limiting example, there may be four independentsensors housed in and/or on the battery pack 108 measuring temperature,electrical characteristic such as voltage, amperage, resistance, orimpedance, or any other parameters and/or quantities as described inthis disclosure. In an embodiment, use of a plurality of independentsensors may result in redundancy configured to employ more than onesensor that measures the same phenomenon, those sensors being of thesame type, a combination of, or another type of sensor not disclosed, sothat in the event one sensor fails, the ability of the computing device104, flight controller, and/or user to detect phenomenon is maintainedand in a non-limiting example, a user alter aircraft usage pursuant tosensor readings.

Additionally, or alternatively, and still referring to FIG. 1. In oneembodiment, at least a sensor 112 may include a moisture sensor.“Moisture”, as used in this disclosure, is the presence of water, thismay include vaporized water in air, condensation on the surfaces ofobjects, or concentrations of liquid water. Moisture may includehumidity. “Humidity”, as used in this disclosure, is the property of agaseous medium (almost always air) to hold water in the form of vapor.An amount of water vapor contained within a parcel of air can varysignificantly. Water vapor is generally invisible to the human eye andmay be damaging to electrical components. There are three primarymeasurements of humidity, absolute, relative, specific humidity.“Absolute humidity,” for the purposes of this disclosure, describes thewater content of air and is expressed in either grams per cubic metersor grams per kilogram. “Relative humidity”, for the purposes of thisdisclosure, is expressed as a percentage, indicating a present stat ofabsolute humidity relative to a maximum humidity given the sametemperature. “Specific humidity”, for the purposes of this disclosure,is the ratio of water vapor mass to total moist air parcel mass, whereparcel is a given portion of a gaseous medium. Moisture sensor may bepsychrometer. Moisture sensor may be a hygrometer. Moisture sensor maybe configured to act as or include a humidistat. A “humidistat”, for thepurposes of this disclosure, is a humidity-triggered switch, often usedto control another electronic device. Moisture sensor may usecapacitance to measure relative humidity and include in itself, or as anexternal component, include a device to convert relative humiditymeasurements to absolute humidity measurements. “Capacitance”, for thepurposes of this disclosure, is the ability of a system to store anelectric charge, in this case the system is a parcel of air which may benear, adjacent to, or above a battery cell.

With continued reference to FIG. 1, at least a sensor 112 may includeelectrical sensors. Electrical sensors may be configured to measurevoltage across a component, electrical current through a component, andresistance of a component. Electrical sensors may include separatesensors to measure each of the previously disclosed electricalcharacteristics such as voltmeter, ammeter, and ohmmeter, respectively.As disclosed above, at least a sensor may be configured to measurephysical and/or electrical phenomena and characteristics of a batterypack, in whole or in part. The at least a senor, in some embodiments,may be further configured to transmit electric signals to a data storagesystem to be save. In nonlimiting embodiments, battery electricalphenomena may be continuously measured and stored at an intermediarystore location, and then permanently save by a data storage system at alater time. In some embodiments, at least a sensor 112 may include oneor more sensors of the same type used to measure the same electricalphenomena, as to provide redundancy, so in the event one of sensorsfails, functionality of system 100 is maintained. In some embodiments,at least a sensor 112 may include different types of sensors measuringthe same electric phenomena as to provide redundancy in case of sensorfailure. In a nonlimiting example, one sensor continues to measure thebattery voltage when another sensor stops working. Measuring electricalparameters may be consistent with any embodiment described inNon-provisional application Ser. No. 16/598,307 filed on Oct. 10, 2019and entitled “METHODS AND SYSTEMS FOR ALTERING POWER DURING FLIGHT,”Non-provisional application Ser. No. 16/599,538 filed on Oct. 11, 2019and entitled “SYSTEMS AND METHODS FOR IN-FLIGHT OPERATIONAL ASSESSMENT,”and Non-provisional application Ser. No. 16/590,496 filed on Oct. 2,2019 and entitled “SYSTEMS AND METHODS FOR RESTRICTING POWER TO A LOADTO PREVENT ENGAGING CIRCUIT PROTECTION DEVICE FOR AN AIRCRAFT,” all ofwhich are incorporated herein by reference in their entirety.

Alternatively or additionally, and with continued reference to FIG. 1,at least a sensor 112 may include a sensor or plurality thereof that maydetect voltage and direct the charging of individual battery cellsaccording to charge level; detection may be performed using any suitablecomponent, set of components, and/or mechanism for direct or indirectmeasurement and/or detection of voltage levels, including withoutlimitation comparators, analog to digital converters, any form ofvoltmeter, or the like. At least a sensor 112 and/or a control circuitincorporated therein and/or communicatively connected thereto may beconfigured to adjust charge to one or more battery cells as a functionof a charge level and/or a detected parameter. For instance, and withoutlimitation, at least a sensor 112 may be configured to determine that acharge level of a battery cell is high based on a detected voltage levelof that battery cell or portion of the battery pack. At least a sensor112 may alternatively or additionally detect a charge reduction event,defined for purposes of this disclosure as any temporary or permanentstate of a battery cell requiring reduction or cessation of charging; acharge reduction event may include a cell being fully charged and/or acell undergoing a physical and/or electrical process that makescontinued charging at a current voltage and/or current level inadvisabledue to a risk that the cell will be damaged, will overheat, or the like.Detection of a charge reduction event may include detection of atemperature, of the cell above a threshold level, detection of a voltageand/or resistance level above or below a threshold, or the like. Atleast a sensor 112 may include digital sensors, analog sensors, or acombination thereof. At least a sensor 112 may include digital-to-analogconverters (DAC), analog-to-digital converters (ADC, A/D, A-to-D), acombination thereof, or other signal conditioning components used intransmission of a first plurality of battery pack 108 data to adestination over wireless or wired connection.

With continued reference to FIG. 1, at least a sensor 112 may includethermocouples, thermistors, thermometers, passive infrared sensors,resistance temperature sensors (RTD's), semiconductor based integratedcircuits (IC), a combination thereof or another undisclosed sensor type,alone or in combination. Temperature, for the purposes of thisdisclosure, and as would be appreciated by someone of ordinary skill inthe art, is a measure of the heat energy of a system. Temperature, asmeasured by any number or at least a sensor 112, may be measured inFahrenheit (° F.), Celsius (° C.), Kelvin (° K), or another scale aloneor in combination. The temperature measured by sensors may compriseelectrical signals which are transmitted to their appropriatedestination wireless or through a wired connection.

Additionally, or alternatively, at least a sensor 112 may include asensor configured to detect gas that may be emitted during or after acell failure. “Cell failure”, for the purposes of this disclosure,refers to a malfunction of a battery cell, which may be anelectrochemical cell, that renders the cell inoperable for its designedfunction, namely providing electrical energy to at least a portion of anelectric aircraft. Byproducts of cell failure may include gaseousdischarge including oxygen, hydrogen, carbon dioxide, methane, carbonmonoxide, a combination thereof, or another undisclosed gas, alone or incombination. Further the sensor configured to detect vent gas fromelectrochemical cells may comprise a gas detector. For the purposes ofthis disclosure, a “gas detector” is a device used to detect a gas ispresent in an area. Gas detectors, and more specifically, the gassensor, may be configured to detect combustible, flammable, toxic,oxygen depleted, a combination thereof, or another type of gas alone orin combination. The gas sensor may include a combustible gas,photoionization detectors, electrochemical gas sensors, ultrasonicsensors, metal-oxide-semiconductor (MOS) sensors, infrared imagingsensors, a combination thereof, or another undisclosed type of gassensor alone or in combination. At least a sensor 112 may includesensors that are configured to detect non-gaseous byproducts of cellfailure including, in non-limiting examples, liquid chemical leaksincluding aqueous alkaline solution, ionomer, molten phosphoric acid,liquid electrolytes with redox shuttle and ionomer, and salt water,among others. At least a sensor may include sensors that are configuredto detect non-gaseous byproducts of cell failure including, innon-limiting examples, electrical anomalies as detected by any of theprevious disclosed sensors or components.

With continued reference to FIG. 1, at least a sensor 112 may beconfigured to detect events where voltage nears an upper voltagethreshold or lower voltage threshold. The upper voltage threshold may bestored in data storage system for comparison with an instant measurementtaken by at least a sensor 112. The upper voltage threshold may becalculated and calibrated based on factors relating to battery cellhealth, maintenance history, location within battery pack 108, designedapplication, and type, among others. At least a sensor 112 may measurevoltage at an instant, over a period of time, or periodically. At leasta sensor 112 may be configured to operate at any of these detectionmodes, switch between modes, or simultaneous measure in more than onemode. At least a sensor 112 may detect events where voltage nears thelower voltage threshold. The lower voltage threshold may indicate powerloss to or from an individual battery cell or portion of the batterypack. At least a sensor 112 may detect events where voltage exceeds theupper and lower voltage threshold. Events where voltage exceeds theupper and lower voltage threshold may indicate battery cell failure orelectrical anomalies that could lead to potentially dangerous situationsfor aircraft and personnel that may be present in or near its operation.

Still referring to FIG. 1, computing device 104 is configured togenerate a useful energy remaining datum as a function of the internalstate datum and a battery model. A “useful energy remaining datum,” forthe purpose of this disclosure, is a datum describing a quantity ofenergy remaining in the battery. In one embodiment, the useful energyremaining datum may be generated using an algorithm, such that thealgorithm uses the internal state datum and the battery model 116 as aninput and outputs the useful energy remaining datum. In one embodiment,the useful energy remaining datum may be generated as a function of amachine learning process 120. In one embodiment, computing device 104 isfurther configured to utilize neural networks to generate the usefulenergy remaining datum. Battery model 116 may include a plurality ofbattery models capable of producing at least the expected value ofenergy remaining in the battery and/or a state of charge curve of thebattery. In a nonlimiting example, an electrochemical modeling, such asa “Newman Pseudo 2D model”, may be used. In one embodiment, the batterymodel may include a heat generation model. In some embodiments, thebattery model 116 may be produced as a function of a machine learningprocess 120. Machine learning model and neural network are described inmore detail further below.

Additionally, or alternatively, and continuing to refer to FIG. 1. Inembodiments, machine learning process may be trained using a set oftraining data. “Training data” may include battery internal state datumcorrelated to remaining useful energy datum. In an embodiment, trainingdata may include past correlations of internal state datum and remaininguseful energy datum for the same aircraft or may include pastcorrelations for other electric aircrafts. In some embodiment, trainingdata may be stored in a data store system coupled to the electricaircraft. In some embodiments, data store system may be a remotedatabase communicatively connected to system 100. In a nonlimitingexample, electric aircraft includes a database containing training datathat is updated against a remote database when it becomescommunicatively connected to the remote database, or at preset timeintervals. In some embodiments, system 100 may be configured to updatetraining data database whenever the system 100 generates a useful energyremaining datum. In an embodiment, system 100 may be configured tocorrelate data whenever it generates remaining useful energy datum. Insome embodiment, system 100 may further be configured to storecorrelated data in a local data store system. In some embodiments,system 100 may further be configured to update training data databasewith correlated data. In some embodiments, battery model and/or machinelearning model may be trained as a function of the training data. Thebattery model and machine learning model, and training those models, areconsistent with the machine learning model, and neural network,described further below.

Alternatively, or additionally, and still referring to FIG. 1. Computingdevice 104 may be configured to generate a thermal datum as a functionof the internal state datum and the battery model 116. In oneembodiment, computing device 104 is further configured to calculate aprobability of a thermal runaway as a function of the generated thermaldatum. In one embodiment, calculating the probability of a thermalrunaway includes utilizing a machine learning process 120. In oneembodiment, calculating the thermal runaway may further includeutilizing a neural network. In a nonlimiting example, based on anobserved increase in the battery's temperature and data from a pluralityof sources, such as other observed thermal runaway events, a machinelearning module may be able to predict when a thermal runaway may occurbased on the observed temperature and the environment surrounding theelectric aircraft.

Still referring to FIG. 1, computing device 104 is configured to displaythe useful energy remaining datum to a user. In one embodiment,computing device 104 may be configured to display a remaining flightrange based on the useful energy remaining datum. In one embodiment,remaining flight range may be calculated as a function of a flight planand the remaining useful energy datum. In one embodiment, the flightrange remaining may be determined as a function of a flight path. Insome embodiments, computing device 104 may determine and displayremaining flight ranges based on multiple cruising speeds. In anembodiment, computing device 104 may display a flight range for acruising speed as a function of a flight path. In a nonlimiting example,the computing device 104 may determine based on a set flight path thatthe aircraft can only reach a destination if it flies at specificcruising speeds based on the remaining flight range and may only displayremaining flight ranges for those speeds. In another nonlimitingexample, the computing device 104 may be further configured to display awarning for cruising speeds that will not allow the aircraft to reach itset destination based on the flight path. Remaining flight range may bedetermined according to any embodiments described in Non-provisionalapplication Ser. No. 17/108,798 filed on Dec. 1, 2020 and entitled“SYSTEMS AND METHODS FOR A BATTERY MANAGEMENT SYSTEM INTEGRATED IN ABATTERY PACK CONFIGURED FOR USE IN ELECTRIC AIRCRAFT,” which isincorporated herein by reference in its entirety.

In some embodiments, and continuing to refer to FIG. 1, the remainingflight range may be generated using an algorithm, such that thealgorithm uses the remaining useful energy datum and a power consumptionrate as an input and outputs the remaining flight range. In someembodiments, the remaining flight range may be determined as a functiona machine learning process 120. In an embodiment, computing device 104is further configured to utilize neural networks to determine theremaining flight range. In one embodiment, training data may includepast correlations of remaining useful energy datum and power consumptionrates. In some embodiments, training data includes correlation for powerconsumption rates for multiple cruising speeds. In embodiments, trainingdata includes correlations between remaining flight range and flightpath. In some embodiments, training data may include correlations ratefor the same aircraft or may include past correlations for otherelectric aircrafts. In some embodiment, training data may be stored in adata store system coupled to the electric aircraft. In some embodiments,data store system may be a remote database communicatively connected tosystem 100. In some embodiments, system 100 may be configured to updatetraining data database whenever the system 100 generates a useful energyremaining datum. In an embodiment, system 100 may be configured tocorrelate data whenever it generates remaining useful energy datum. Insome embodiment, system 100 may further be configured to storecorrelated data in a local data store system. In some embodiments,system 100 may further be configured to update training data databasewith correlated data. In some embodiments, the machine learning modelmay be trained as a function of the training data.

Additionally, or alternatively, and still referring to FIG. 1. In someembodiment, computing device 104 may be configured to display the depthof discharge based on the useful energy remaining datum. “Depth ofdischarge”, as referred herein for the purpose of this disclosure,refers to the percentage of the battery that has being discharged, orthe percentage that is not useful energy, that is determined as afunction of the useful energy remaining datum. In a nonlimiting example,the computing device 104 may display to a user the remaining usefulenergy and a percentage of the battery that is devoid of energy or hasenergy that cannot be used. Any computing device capable of displayinginformation to the user may be used to display the useful energyremaining datum. Computing device used to display the data may include agraphical user interface, multi-function display (MFD), primary display,gauges, graphs, audio cues, visual cues, information on a heads-updisplay (HUD) or a combination thereof. The display may include adisplay 124 disposed in one or more areas of an aircraft, on a userdevice remotely located, one or more computing devices, or a combinationthereof. The display 124 may be disposed in a projection, hologram, orscreen within a user's helmet, eyeglasses, contact lens, or acombination thereof. The display 124 may include portable devices suchas laptops, computer tablets, smartphones, smartwatches, PDAs, and thelike. In one embodiment, the computing device 104 may be furtherconfigured to display the calculated thermal runaway prediction. In anonlimiting example, a user may visualize the useful energy remainingdatum from inside the electric airplane. In another nonlimiting example,a fleet manager may visualize the data through a remote device.

Alternatively, or additionally, and still referring to FIG. 1, computingdevice 104 may be configured to display a warning to the user based onthe calculated thermal runaway prediction. In a nonlimiting example, auser may get a warning to land the aircraft before the thermal runawayis predicted to occur.

Now referring to FIG. 2, exemplary embodiment of a method 200 fordetermining remaining useful energy in an electric aircraft isillustrated. At step 205, method 200 includes measuring, by a computingdevice 104, a internal state datum of a battery 108 as a function of atleast a sensor 112.

Still referring to FIG. 2, at step 210, method 200 includes receiving,by the computing device 104, the internal state datum from the at leasta sensor 112.

Continuing to refer to FIG. 2, at step 215, method 200 includesgenerating, by the computing device 104, a useful energy remaining datumas a function of the internal state datum and a battery model 116. Inone embodiment, method 200 may further include generating the usefulenergy remaining datum as a function of a machine learning process 120.In one embodiment, method 200 may further include utilizing a neuralnetwork. In one embodiment, method 200 may further include generating abattery degradation rate as a function of the internal state datum andthe battery model 116. In some embodiments, method 200 may furtherinclude generating a thermal datum as a function of the internal statedatum and a battery model 116. In one embodiment, method 200 may includecalculating a probability of a thermal runaway as a function of thethermal datum.

Still referring to FIG. 2, at step 220, method 200 includes displaying,by the computing device 104, the useful energy remaining datum to auser. In one embodiment, method 200 may further include displaying, bythe computing device, a depth of discharge of the battery. In oneembodiment, method 200 may further include displaying a batterydegradation rate. In one embodiment, method 200 may further includedisplaying a warning related to probability of thermal runaway. In oneembodiment, method 200 may further include displaying a remaining flightrange of based on the useful energy remaining datum.

Now referring to FIG. 3, an exemplary embodiment of an eVTOL aircraftbattery pack is illustrated. Battery pack 108 is a power source that maybe configured to store electrical energy in the form of a plurality ofbattery modules, which themselves include of a plurality ofelectrochemical cells. These cells may utilize electrochemical cells,galvanic cells, electrolytic cells, fuel cells, flow cells, and/orvoltaic cells. In general, an electrochemical cell is a device capableof generating electrical energy from chemical reactions or usingelectrical energy to cause chemical reactions, this disclosure willfocus on the former. Voltaic or galvanic cells are electrochemical cellsthat generate electric current from chemical reactions, whileelectrolytic cells generate chemical reactions via electrolysis. Ingeneral, the term ‘battery’ is used as a collection of cells connectedin series or parallel to each other. A battery cell may, when used inconjunction with other cells, may be electrically connected in series,in parallel or a combination of series and parallel. Series connectionincludes wiring a first terminal of a first cell to a second terminal ofa second cell and further configured to include a single conductive pathfor electricity to flow while maintaining the same current (measured inAmperes) through any component in the circuit. A battery cell may usethe term ‘wired’, but one of ordinary skill in the art would appreciatethat this term is synonymous with ‘electrically connected’, and thatthere are many ways to couple electrical elements like battery cellstogether. An example of a connector that does not include wires may beprefabricated terminals of a first gender that mate with a secondterminal with a second gender. Battery cells may be wired in parallel.Parallel connection includes wiring a first and second terminal of afirst battery cell to a first and second terminal of a second batterycell and further configured to include more than one conductive path forelectricity to flow while maintaining the same voltage (measured inVolts) across any component in the circuit. Battery cells may be wiredin a series-parallel circuit which combines characteristics of theconstituent circuit types to this combination circuit. Battery cells maybe electrically connected in a virtually unlimited arrangement which mayconfer onto the system the electrical advantages associated with thatarrangement such as high-voltage applications, high-currentapplications, or the like. In an exemplary embodiment, battery pack 108include 196 battery cells in series and 18 battery cells in parallel.This is, as someone of ordinary skill in the art would appreciate, isonly an example and battery pack 108 may be configured to have a nearlimitless arrangement of battery cell configurations.

With continued reference to FIG. 3, battery pack 108 may include aplurality of battery modules. The battery modules may be wired togetherin series and in parallel. Battery pack 108 may include a center sheetwhich may include a thin barrier. The barrier may include a fuseconnecting battery modules on either side of the center sheet. The fusemay be disposed in or on the center sheet and configured to connect toan electric circuit comprising a first battery module and thereforebattery unit and cells. In general, and for the purposes of thisdisclosure, a fuse is an electrical safety device that operate toprovide overcurrent protection of an electrical circuit. As asacrificial device, its essential component is metal wire or strip thatmelts when too much current flows through it, thereby interruptingenergy flow. The fuse may include a thermal fuse, mechanical fuse, bladefuse, expulsion fuse, spark gap surge arrestor, varistor, or acombination thereof.

Still referring to FIG. 3. Battery pack 108 may also include a side wallincludes a laminate of a plurality of layers configured to thermallyinsulate the plurality of battery modules from external components ofbattery pack 108. The side wall layers may include materials whichpossess characteristics suitable for thermal insulation as described inthe entirety of this disclosure like fiberglass, air, iron fibers,polystyrene foam, and thin plastic films, to name a few. The side wallmay additionally or alternatively electrically insulate the plurality ofbattery modules from external components of battery pack 108 and thelayers of which may include polyvinyl chloride (PVC), glass, asbestos,rigid laminate, varnish, resin, paper, Teflon, rubber, and mechanicallamina. The center sheet may be mechanically coupled to the side wall inany manner described in the entirety of this disclosure or otherwiseundisclosed methods, alone or in combination. The side wall may includea feature for alignment and coupling to the center sheet. This featuremay include a cutout, slots, holes, bosses, ridges, channels, and/orother undisclosed mechanical features, alone or in combination.

With continued reference to FIG. 3, battery pack 108 may also include anend panel including a plurality of electrical connectors and furtherconfigured to fix battery pack 108 in alignment with at least the sidewall. The end panel may include a plurality of electrical connectors ofa first gender configured to electrically and mechanically couple toelectrical connectors of a second gender. The end panel may beconfigured to convey electrical energy from battery cells to at least aportion of an eVTOL aircraft. Electrical energy may be configured topower at least a portion of an eVTOL aircraft or include signals tonotify aircraft computers, personnel, users, pilots, and any others ofinformation regarding battery health, emergencies, and/or electricalcharacteristics. The plurality of electrical connectors may includeblind mate connectors, plug and socket connectors, screw terminals, ringand spade connectors, blade connectors, and/or an undisclosed type aloneor in combination. The electrical connectors of which the end panelincludes may be configured for power and communication purposes. A firstend of the end panel may be configured to mechanically couple to a firstend of a first side wall by a snap attachment mechanism, similar to endcap and side panel configuration utilized in the battery module. Toreiterate, a protrusion disposed in or on the end panel may be captured,at least in part, by a receptacle disposed in or on the side wall. Asecond end of end the panel may be mechanically coupled to a second endof a second side wall in a similar or the same mechanism.

With continued reference to FIG. 3, at least a sensor 112 may bedisposed in or on a portion of battery pack 108 near battery modules orbattery cells. Battery pack 108 includes battery management system headunit disposed on a first end of battery pack 108. Battery managementsystem head unit is configured to communicate with a flight controllerusing a controller area network (CAN). Controller area network includesbus. Bus may include an electrical bus. “Bus”, for the purposes of thisdisclosure and in electrical parlance is any common connection to whichany number of loads, which may be connected in parallel, and share arelatively similar voltage may be electrically coupled. Bus may refer topower busses, audio busses, video busses, computing address busses,and/or data busses. Bus may be responsible for conveying electricalenergy stored in battery pack 108 to at least a portion of an electricaircraft. Bus may be additionally or alternatively responsible forconveying electrical signals generated by any number of componentswithin battery pack 108 to any destination on or offboard an electricaircraft. Battery management system head unit may comprise wiring orconductive surfaces only in portions required to electrically couple busto electrical power or necessary circuits to convey that power orsignals to their destinations.

Outputs from sensors or any other component present within system may beanalog or digital. Onboard or remotely located processors can convertthose output signals from sensor suite to a usable form by thedestination of those signals. The usable form of output signals fromsensors, through processor may be either digital, analog, a combinationthereof or an otherwise unstated form. Processing may be configured totrim, offset, or otherwise compensate the outputs of sensor suite. Basedon sensor output, the processor can determine the output to send todownstream component. Processor can include signal amplification,operational amplifier (OpAmp), filter, digital/analog conversion,linearization circuit, current-voltage change circuits, resistancechange circuits such as Wheatstone Bridge, an error compensator circuit,a combination thereof or otherwise undisclosed components.

With continued reference to FIG. 3, any of the disclosed components orsystems, namely battery pack 108, and/or battery cells may incorporateprovisions to dissipate heat energy present due to electrical resistancein integral circuit. Battery pack 108 includes one or more batteryelement modules wired in series and/or parallel. The presence of avoltage difference and associated amperage inevitably will increase heatenergy present in and around battery pack 108 as a whole. The presenceof heat energy in a power system is potentially dangerous by introducingenergy possibly sufficient to damage mechanical, electrical, and/orother systems present in at least a portion of an electric aircraft.Battery pack 108 may include mechanical design elements, one of ordinaryskill in the art, may thermodynamically dissipate heat energy away frombattery pack 108. The mechanical design may include, but is not limitedto, slots, fins, heat sinks, perforations, a combination thereof, oranother undisclosed element.

Heat dissipation may include material selection beneficial to move heatenergy in a suitable manner for operation of battery pack 108. Certainmaterials with specific atomic structures and therefore specificelemental or alloyed properties and characteristics may be selected inconstruction of battery pack 108 to transfer heat energy out of avulnerable location or selected to withstand certain levels of heatenergy output that may potentially damage an otherwise unprotectedcomponent. One of ordinary skill in the art, after reading the entiretyof this disclosure would understand that material selection may includetitanium, steel alloys, nickel, copper, nickel-copper alloys such asMonel, tantalum and tantalum alloys, tungsten and tungsten alloys suchas Inconel, a combination thereof, or another undisclosed material orcombination thereof. Heat dissipation may include a combination ofmechanical design and material selection. The responsibility of heatdissipation may fall upon the material selection and design as disclosedabove in regard to any component disclosed in this paper. The batterypack 108 may include similar or identical features and materialsascribed to battery pack 108 in order to manage the heat energy producedby these systems and components.

According to embodiments, the circuitry disposed within or on batterypack 108 may be shielded from electromagnetic interference. The batteryelements and associated circuitry may be shielded by material such asmylar, aluminum, copper a combination thereof, or another suitablematerial. The battery pack 108 and associated circuitry may include oneor more of the aforementioned materials in their inherent constructionor additionally added after manufacture for the express purpose ofshielding a vulnerable component. The battery pack 108 and associatedcircuitry may alternatively or additionally be shielded by location.Electrochemical interference shielding by location includes a designconfigured to separate a potentially vulnerable component from energythat may compromise the function of said component. The location ofvulnerable component may be a physical uninterrupted distance away froman interfering energy source, or location configured to include ashielding element between energy source and target component. Theshielding may include an aforementioned material in this section, amechanical design configured to dissipate the interfering energy, and/ora combination thereof. The shielding comprising material, location andadditional shielding elements may defend a vulnerable component from oneor more types of energy at a single time and instance or includeseparate shielding for individual potentially interfering energies.

Referring now to FIG. 4, an embodiment of an electric aircraft 400 ispresented. The electric aircraft 400 may include a vertical takeoff andlanding aircraft (eVTOL). As used herein, a vertical take-off andlanding (eVTOL) aircraft is one that can hover, take off, and landvertically. An eVTOL, as used herein, is an electrically poweredaircraft typically using an energy source, of a plurality of energysources to power the aircraft. In order to optimize the power and energynecessary to propel the aircraft. eVTOL may be capable of rotor-basedcruising flight, rotor-based takeoff, rotor-based landing, fixed-wingcruising flight, airplane-style takeoff, airplane-style landing, and/orany combination thereof. Rotor-based flight, as described herein, iswhere the aircraft generated lift and propulsion by way of one or morepowered rotors coupled with an engine, such as a “quad copter,”multi-rotor helicopter, or other vehicle that maintains its liftprimarily using downward thrusting propulsors. Fixed-wing flight, asdescribed herein, is where the aircraft is capable of flight using wingsand/or foils that generate life caused by the aircraft's forwardairspeed and the shape of the wings and/or foils, such as airplane-styleflight.

With continued reference to FIG. 4, a number of aerodynamic forces mayact upon the electric aircraft 400 during flight. Forces acting on anelectric aircraft 400 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 400 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 400 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 400 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 400 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 400 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 400 downward due to the force of gravity. Anadditional force acting on electric aircraft 400 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor of the electric aircraft. Liftgenerated by the airfoil may depend on speed of airflow, density of air,total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 400 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of an electric aircraft400, including without limitation propulsors and/or propulsionassemblies. In an embodiment, the motor may eliminate need for manyexternal structural features that otherwise might be needed to join onecomponent to another component. The motor may also increase energyefficiency by enabling a lower physical propulsor profile, reducing dragand/or wind resistance. This may also increase durability by lesseningthe extent to which drag and/or wind resistance add to forces acting onelectric aircraft 400 and/or propulsors.

Still referring to FIG. 4, electric aircraft 400 may include at least asensor 112 coupled to the electric aircraft. In one embodiment, electricaircraft 400 may include a flight controller, where the flightcontroller may be configured to generate the remaining useful energy inthe battery 108 of the electric aircraft 400 as a function of the datatransmitted by the at least a sensor 112.

Now referring to FIG. 5, an exemplary embodiment 500 of a flightcontroller 504 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 504 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 504may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 504 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 5, flight controller 504may include a signal transformation component 508. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 508 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component508 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 508 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 508 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 508 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 5, signal transformation component 508 may beconfigured to optimize an intermediate representation 512. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 508 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 508 may optimizeintermediate representation 512 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 508 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 508 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 504. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

Still referring to FIG. 5. In an embodiment, and without limitation,signal transformation component 508 may include transform one or moreinputs and outputs as a function of an error correction code. An errorcorrection code, also known as error correcting code (ECC), is anencoding of a message or lot of data using redundant information,permitting recovery of corrupted data. An ECC may include a block code,in which information is encoded on fixed-size packets and/or blocks ofdata elements such as symbols of predetermined size, bits, or the like.Reed-Solomon coding, in which message symbols within a symbol set havingq symbols are encoded as coefficients of a polynomial of degree lessthan or equal to a natural number k, over a finite field F with qelements; strings so encoded have a minimum hamming distance of k+1, andpermit correction of (q−k−1)/2 erroneous symbols. Block code mayalternatively or additionally be implemented using Golay coding, alsoknown as binary Golay coding, Bose-Chaudhuri, Hocquenghuem (BCH) coding,multidimensional parity-check coding, and/or Hamming codes. An ECC mayalternatively or additionally be based on a convolutional code.

In an embodiment, and still referring to FIG. 5, flight controller 504may include a reconfigurable hardware platform 516. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 516 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 5, reconfigurable hardware platform 516 mayinclude a logic component 520. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 520 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 520 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 520 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 520 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 520 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 512. Logiccomponent 520 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 504. Logiccomponent 520 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 520 may beconfigured to execute the instruction on intermediate representation 512and/or output language. For example, and without limitation, logiccomponent 520 may be configured to execute an addition operation onintermediate representation 512 and/or output language.

With continued reference to FIG. 5. In an embodiment, and withoutlimitation, logic component 520 may be configured to calculate a flightelement 524. As used in this disclosure a “flight element” is an elementof datum denoting a relative status of aircraft. For example, andwithout limitation, flight element 524 may denote one or more torques,thrusts, airspeed velocities, forces, altitudes, groundspeed velocities,directions during flight, directions facing, forces, orientations, andthe like thereof. For example, and without limitation, flight element524 may denote that aircraft is cruising at an altitude and/or with asufficient magnitude of forward thrust. As a further non-limitingexample, flight status may denote that is building thrust and/orgroundspeed velocity in preparation for a takeoff. As a furthernon-limiting example, flight element 524 may denote that aircraft isfollowing a flight path accurately and/or sufficiently.

Still referring to FIG. 5, flight controller 504 may include a chipsetcomponent 528. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 528 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 520 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 528 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 520 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 528 maymanage data flow between logic component 520, memory cache, and a flightcomponent 532. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 532 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component532 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 528 may be configured to communicate witha plurality of flight components as a function of flight element 524.For example, and without limitation, chipset component 528 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 5, flight controller 504may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 504 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 524. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 504 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 504 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 5, flight controller 504may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 524 and a pilot signal536 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 536may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 536 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 536may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 536 may include an explicitsignal directing flight controller 504 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 536 may include an implicit signal, wherein flight controller 504detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 536 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 536 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 536 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 536 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal536 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 5, autonomous machine-learning model may includeone or more autonomous machine-learning processes such as supervised,unsupervised, or reinforcement machine-learning processes that flightcontroller 504 and/or a remote device may or may not use in thegeneration of autonomous function. As used in this disclosure “remotedevice” is an external device to flight controller 504. Additionally oralternatively, autonomous machine-learning model may include one or moreautonomous machine-learning processes that a field-programmable gatearray (FPGA) may or may not use in the generation of autonomousfunction. Autonomous machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 5, autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 504 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 5, flight controller 504 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 504. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 504 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, a autonomous machine-learning process correction, andthe like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 504 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 5, flight controller 504 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 5, flight controller 504may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller504 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 504 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 504 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 5, control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 532. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 5, the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 504. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 512 and/or output language from logiccomponent 520, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 5, master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 5, control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 5, flight controller 504 may also be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of aircraft and/orcomputing device. Flight controller 504 may include a distributer flightcontroller. As used in this disclosure a “distributer flight controller”is a component that adjusts and/or controls a plurality of flightcomponents as a function of a plurality of flight controllers. Forexample, distributer flight controller may include a flight controllerthat communicates with a plurality of additional flight controllersand/or clusters of flight controllers. In an embodiment, distributedflight control may include one or more neural networks. For example,neural network also known as an artificial neural network, is a networkof “nodes,” or data structures having one or more inputs, one or moreoutputs, and a function determining outputs based on inputs. Such nodesmay be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 5, flight controller may include asub-controller 540. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 504 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 540may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 540 may include any component of any flightcontroller as described above. Sub-controller 540 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 540may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 540 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 5, flight controller may include a co-controller544. As used in this disclosure a “co-controller” is a controller and/orcomponent that joins flight controller 504 as components and/or nodes ofa distributer flight controller as described above. For example, andwithout limitation, co-controller 544 may include one or morecontrollers and/or components that are similar to flight controller 504.As a further non-limiting example, co-controller 544 may include anycontroller and/or component that joins flight controller 504 todistributer flight controller. As a further non-limiting example,co-controller 544 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data to and/or from flight controller 504 to distributedflight control system. Co-controller 544 may include any component ofany flight controller as described above. Co-controller 544 may beimplemented in any manner suitable for implementation of a flightcontroller as described above.

In an embodiment, and with continued reference to FIG. 5, flightcontroller 504 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 504 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 6, an exemplary embodiment of a machine-learningmodule 600 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 604 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 608 given data provided as inputs 612;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 6, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 604 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 604 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 604 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 604 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 604 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 604 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data604 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 6,training data 604 may include one or more elements that are notcategorized; that is, training data 604 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 604 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 604 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 604 used by machine-learning module 600 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 6, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 616. Training data classifier 616 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 600 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 604. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 6, machine-learning module 600 may be configuredto perform a lazy-learning process 620 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 604. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 604elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 6,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 624. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 624 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 624 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 604set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 6, machine-learning algorithms may include atleast a supervised machine-learning process 628. At least a supervisedmachine-learning process 628, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 604. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process628 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 6, machine learning processes may include atleast an unsupervised machine-learning processes 632. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 6, machine-learning module 600 may be designedand configured to create a machine-learning model 624 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 6, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 7, an exemplary embodiment of neural network 700is illustrated. A neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes 704 may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes 704, one or more intermediate layers, and an output layer of nodes704. Connections between nodes 704 may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Referring now to FIG. 8, an exemplary embodiment of a node 800 of aneural network 700 is illustrated. A node may include, withoutlimitation a plurality of inputs x_(n) 804 that may receive numericalvalues from inputs to a neural network containing the node and/or fromother nodes. Node may perform a weighted sum of inputs using weightsw_(n) 808 that are multiplied by respective inputs x_(n) 804.Additionally or alternatively, a bias b 812 may be added to the weightedsum of the inputs such that an offset is added to each unit in theneural network layer that is independent of the input to the layer. Theweighted sum may then be input into a function φ 816, which may generateone or more outputs y 820. Weight w_(n) 808 applied to an input x_(n)804 may indicate whether the input is “excitatory,” indicating that ithas strong influence on the one or more outputs y, for instance by thecorresponding weight having a large numerical value, and/or a“inhibitory,” indicating it has a weak effect influence on the one moreinputs y 820, for instance by the corresponding weight having a smallnumerical value. The values of weights w_(n) 808 may be determined bytraining a neural network using training data, which may be performedusing any suitable process as described above. In an embodiment, andwithout limitation, a neural network may receive semantic units asinputs and output vectors representing such semantic units according toweights w_(n) 808 that are derived using machine-learning processes asdescribed in this disclosure.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 9 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 900 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 900 includes a processor 904 and a memory908 that communicate with each other, and with other components, via abus 912. Bus 912 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 904 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 904 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 904 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 908 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 916 (BIOS), including basic routines that help totransfer information between elements within computer system 900, suchas during start-up, may be stored in memory 908. Memory 908 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 920 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 908 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 900 may also include a storage device 924. Examples of astorage device (e.g., storage device 924) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 924 may be connected to bus 912 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 924 (or one or morecomponents thereof) may be removably interfaced with computer system 900(e.g., via an external port connector (not shown)). Particularly,storage device 924 and an associated machine-readable medium 928 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 900. In one example, software 920 may reside, completelyor partially, within machine-readable medium 928. In another example,software 920 may reside, completely or partially, within processor 904.

Computer system 900 may also include an input device 932. In oneexample, a user of computer system 900 may enter commands and/or otherinformation into computer system 900 via input device 932. Examples ofan input device 932 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 932may be interfaced to bus 912 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 912, and any combinations thereof. Input device 932 mayinclude a touch screen interface that may be a part of or separate fromdisplay 936, discussed further below. Input device 932 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 900 via storage device 924 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 940. A network interfacedevice, such as network interface device 940, may be utilized forconnecting computer system 900 to one or more of a variety of networks,such as network 944, and one or more remote devices 948 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 944,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 920,etc.) may be communicated to and/or from computer system 900 via networkinterface device 940.

Computer system 900 may further include a video display adapter 952 forcommunicating a displayable image to a display device, such as displaydevice 936. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 952 and display device 936 may be utilized incombination with processor 904 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 900 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 912 via a peripheral interface 956. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for determining remaining useful energyin an electric aircraft, the system comprising a computing device,wherein the computing device is configured to: measure an internal statedatum of a battery as a function of at least a sensor, wherein thebattery is on an electric aircraft; receive the internal state datumfrom the at least a sensor; train a machine-learning model usingtraining data stored in a database on the electric aircraft, wherein thetraining data includes battery internal state datum correlated tobattery remaining useful energy data, and wherein the training datastored on the electric aircraft is updated at preset intervals; generatea useful energy remaining datum as a function of the internal statedatum, a battery model and the trained machine-learning model; anddisplay the useful energy remaining datum to a user.
 2. The system ofclaim 1, wherein the computing device is further configured to display adepth of discharge of the battery.
 3. The system of claim 1, wherein thecomputing device is further configured to utilize a neural network. 4.The system of claim 1, wherein the computing device is furtherconfigured to generate a battery degradation rate as a function of theinternal state datum and the battery model.
 5. The system of claim 4,wherein the computing device is further configured to display thebattery degradation rate to the user.
 6. The system of claim 1, whereinthe computing device is further configured to generate a thermal datumas a function of the internal state datum and the battery model.
 7. Thesystem of claim 6, wherein the computing device is configured tocalculate a probability of a thermal runaway as a function of thethermal datum.
 8. The system of claim 7, wherein the computing device isfurther configured to display a warning to the user as a function of thecalculation.
 9. The system of claim 1, wherein the computing device isfurther configured to display a remaining flight range as a function ofthe useful energy remaining datum.
 10. A method for determiningremaining useful energy in an electric aircraft, the method comprising:measuring, by a computing device, an internal state datum of a batteryas a function of at least a sensor, wherein the battery is on anelectric aircraft; receiving, by the computing device, the internalstate datum from the at least a sensor; training, by the computingdevice, a machine-learning model using training data stored in adatabase on the electric aircraft, wherein the training data includesbattery internal state datum correlated to battery remaining usefulenergy data, and wherein the training data stored on the electricaircraft is updated at preset intervals; generating, by the computingdevice, a useful energy remaining datum as a as a function of theinternal state datum, a battery model and the trained machine-learningmodel; and displaying, by the computing device, the useful energyremaining datum to a user.
 11. The method of claim 10, wherein themethod further comprises displaying, by the computing device, a depth ofdischarge of the battery.
 12. The method of claim 10, wherein the methodfurther comprises utilizing, by the computing device, a neural network.13. The method of claim 10, wherein the method further comprisesgenerating, by the computing device, a battery degradation rate as afunction of the internal state datum and the battery model.
 14. Themethod of claim 13, wherein the method further comprises displaying, bythe computing device, the battery degradation rate to the user.
 15. Themethod of claim 10, wherein the method further comprises generating, bythe computing device, a thermal datum as a function of the internalstate datum and the battery model.
 16. The method of claim 15, whereinthe method further comprises calculating, by the computing device, aprobability of a thermal runaway as a function of the thermal datum. 17.The method of claim 16, wherein the method further comprises displaying,by the computing device, a warning to the user as a function of thecalculation.
 18. The method of claim 10, wherein the method furthercomprises displaying, by the computing device, a remaining flight rangeas a function of the useful energy remaining datum.